Fitting and testing carroll's weighted unfolding model for preferences

Mark L. Davison

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


A quadratic programming algorithm is presented for fitting Carroll's weighted unfolding model for preferences to known multidimensional scale values. The algorithm can be applied directly to pairwise preferences; it permits nonnegativity constraints on subject weights; and it provides a means of testing various preference model hypotheses. While basically metric, it can be combined with Kruskal's monotone regression to fit ordinal data. Monte Carlo results show that (a) adequacy of "true" preference recovery depends on the number of data points and the amount of error, and (b) the proportion of data variance accounted for by the model sometimes only approximately reflects "true" recovery.

Original languageEnglish (US)
Pages (from-to)233-247
Number of pages15
Issue number2
StatePublished - Jun 1976
Externally publishedYes


  • multidimensional scaling
  • multiple regression
  • quadratic programming
  • utility theory


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